AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors in Agents

By Weize Chen, Yusheng Su, Jingwei Zuo, Cheng Yang, Chenfei Yuan, Cheng Qian, Chi-Min Chan, Yujia Qin, Ya-Ting Lu, Ruobing Xie and 3 more. International Conference on Learning Representations, 2023

698 citations recorded by the source corpus.

Autonomous agents empowered by Large Language Models (LLMs) have undergone significant improvements, enabling them to generalize across a broad spectrum of tasks. However, in real-world scenarios, cooperation among individuals is often required to enhance the efficiency and effectiveness of task accomplishment. Hence, inspired by human group dynamics, we propose a multi-agent framework \framework that can collaboratively and dynamically adjust its composition as a greater-than-the-sum-of-its-parts system. Our experiments demonstrate that \framework framework can effectively deploy multi-agent groups that outperform a single agent. Furthermore, we delve into the emergence of social behaviors among individual agents within a group during collaborative task accomplishment. In view of these behaviors, we discuss some possible strategies to leverage positive ones and mitigate negative ones for improving the collaborative potential of multi-agent groups. Our codes for \framework will soon be released at \url{https://github.com/OpenBMB/AgentVerse}.

View Paper: https://www.semanticscholar.org/paper/ad97671a924a9b3a060fee857e561f140ec79dd7

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Taicheng Guo
2. Weize Chen
3. Xiuying Chen
4. Yusheng Su
5. Yaqi Wang
6. Cheng Qian
7. Ruidi Chang
8. Dong Huang

Citations in the corpus

• ChatDev: Communicative Agents for Software Development (2023, background): “Furthermore, LLMs have demonstrated strong role-playing abilities (Li et al., 2023a; Park et al., 2023; Hua et al., 2023; Chan et al., 2023; Zhou et al., 2023b; Chen et al., 2023b,a; Cohen et al., 2023; Li et al., 2023b).”
• A survey on large language model based autonomous agents (2023, unknown): “ChemCrow [75], Boiko et al. [115], Grossmann et al. [122] Natural Science Education ChemCrow [75], CodeHelp [120], Boiko et al. [115], MathA-gent [117], Drori et al. [118] Engineering”
• AI Agents vs. Agentic AI: A Conceptual Taxonomy, Applications and Challenges (2025, unknown): “This emerging class of systems extends the capabilities of traditional AI Agents by enabling multiple intelligent entities to collaboratively pursue goals through structured communication [144–146], shared memory [147,148], and dynamic role assignment [22].”
• Large Language Model based Multi-Agents: A Survey of Progress and Challenges (2024, background): “[ Chen et al. , 2023b ] proposes an LLM-based consensus-seeking framework, which can be applied as a co-operative planner to a multi-robot aggregation task.”
• The rise and potential of large language model based agents: a survey (2025, unknown): “This includes enhancing decision-making in traditional agent frameworks, and potentially transforming domains previously dominated by human experts, like legal consultation and medical assistance [326,330].”
• AgentCoder: Multi-Agent-based Code Generation with Iterative Testing and Optimisation (2023, background): “Such benefit of multi-agent collaborations with LLMs has also been illustrated in other multi-agent systems [ Chen et al. , 2023a; Zhang et al. , 2023a].”
• AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation (2023, unknown): “Empirical studies demonstrate the effectiveness of the framework in many example applications, with domains ranging from mathematics, coding, question answering, operations research, online decision-making, entertainment, etc.”
• Multi-View Encoders for Performance Prediction in LLM-Based Agentic Workflows (2025, unknown): “Rather than manually designing a fixed workflow (Qian et al., 2024; Chen et al., 2024; Du et al., 2024) or paying repeated inference costs to synthesize one per query (Zhuge et al., 2024; Liu et al., 2024b), Agen-tic Predictor presents a lightweight performance predictor to rapidly estimate the…”